Military police selection in Canada : an evaluation of the Canadian Forces Military Police Assessment Centre (MPAC)
Bibliographic record
Abstract
In this study, I investigated the psychometric properties of the Military Police Assessment Centre (MPAC) including the incremental predictive ability of job performance by the MPAC beyond the predictive validity of the Canadian Forces Aptitude Test (CFAT), a test of cognitive ability. MPAC data from a sample of Canadian Forces (CF) Military Police (MP) candidates (N=323) were examined using reliability analysis, principle components analysis (PCA), and multitrait-multimethod (MTMM) to assess the reliabilities of the 12 MPAC competencies and six method scales, the MPACs factor structure, and for evidence of convergent and discriminant validity. Job performance data from a sample (N=209) and CFAT data of a sample (N=203) of the 323 candidates were examined using correlation analyses to assess the predictive validity of the MPAC and CFAT. Results of the MPAC reliability analysis indicated that the methods demonstrated higher reliabilities than the competencies. Results of the PCA and MTMM indicated that the MPAC lacked construct validity. The MPAC and the CFAT demonstrated no correlation with the job performance measure. Consequently, the incremental predictive validity of the MPAC beyond the CFAT was not assessed. Recommendations for the use and/or development of a better job performance measure to assess the criterion-related validity of the MPAC are discussed, as are limitations and directions for future research.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".